{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ffc9318d-53fd-4058-a48c-97effea86d06",
   "metadata": {},
   "source": [
    "# <center> 手写体识别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "86f2a763-72ff-4985-a5aa-9b9107a57854",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch \n",
    "import torch.nn as nn #神经网络，fc,cnn,lstm,gru在nn里面\n",
    "from torchvision.datasets.mnist import MNIST#手写体数据集\n",
    "from torch.utils.data import DataLoader#数据加载器\n",
    "from torchvision.transforms import transforms#图像转换相关函数\n",
    "from torchsummary import summary#查看神经网络结构\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0944b52d-0751-46c1-97a2-cb2713fc1ecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')#选择训练的设备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e5733444-c239-4d54-b397-6756f01ab5f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cpu')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d92f2e31-3126-4658-a04f-d471055b3351",
   "metadata": {},
   "source": [
    "## 1.数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e90cb106-0664-40cd-804b-4999ff426135",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_dataset = MNIST(root='./data',\n",
    "                     train=True,\n",
    "                     transform=transforms.Compose([\n",
    "                         transforms.Resize((28, 28)),\n",
    "                         transforms.ToTensor()\n",
    "                     ]),\n",
    "                     download=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d4527ecb-6962-43b5-84bb-223f87f354be",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dataset = MNIST(root='./data',\n",
    "                     train=False,\n",
    "                     transform=transforms.Compose([\n",
    "                         transforms.Resize((28, 28)),\n",
    "                         transforms.ToTensor()\n",
    "                     ]),\n",
    "                     download=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "25b342c6-3688-4210-85ab-af156bd9a8e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 10000)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_dataset), len(test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4a3de09b-a971-47ed-ab02-b2094c5f4e48",
   "metadata": {},
   "outputs": [],
   "source": [
    "image = train_dataset[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b1397d80-a248-4edd-9f56-bc795adfbbd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "image = image.reshape((28, 28))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5c144424-2498-49e1-a679-517d8ccd861c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([28, 28])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "df48e181-4fcb-4738-b92c-5bbbd349aa86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Tensor"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e1c3d650-c0e8-4409-91c4-0a4229308d3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "image = image.numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3e264510-54d8-4080-a589-d4014bfa06c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "38420224-3c79-4690-b3d3-1e2dd2be2ee5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(train_dataset[0][1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ace0289f-6e33-4a40-949a-342f8fe49622",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1aa44682b90>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title(\"label: \" + str(train_dataset[0][1]))\n",
    "plt.imshow(image, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8a6e206c-9323-40a1-9d12-85245dd604c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x800 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(6, 8))\n",
    "for i in range(9):\n",
    "    image = train_dataset[i][0]#图像\n",
    "    image = image.reshape((28, 28)).numpy()#（1,28,28）-->(28,28)\n",
    "    label = str(train_dataset[i][1])#标签\n",
    "    fig.add_subplot(3, 3, i + 1)#添加子图\n",
    "    plt.title(\"label: \" + label)\n",
    "    plt.imshow(image, cmap='gray')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "485f1221-3ee4-4b49-9346-2b2e9c3e6e87",
   "metadata": {},
   "outputs": [],
   "source": [
    "#训练集的数据加载器\n",
    "train_loader = DataLoader(dataset=train_dataset,\n",
    "                         batch_size=32,\n",
    "                         shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "17831bb6-4315-4f60-bd93-b77c8f2ea9bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#测试集的数据加载器\n",
    "test_loader = DataLoader(dataset=test_dataset,\n",
    "                         batch_size=32,\n",
    "                         shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "23d4f192-46d9-4d52-a92d-39bbb7d53fb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1875"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_loader)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78103895-47ee-422a-bae4-33631ad11574",
   "metadata": {},
   "source": [
    "## 2.模型构建"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0cc16faf-c896-49d5-b206-5f8f843ea9bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "class NeuralNetwork(nn.Module):\n",
    "    def __init__(self, num_classes):\n",
    "        super(NeuralNetwork, self).__init__()\n",
    "        self.fc1 = nn.Linear(784, 64)#第一层全连接\n",
    "        self.fc2 = nn.Linear(64, num_classes)#第二层全连接\n",
    "        self.relu = nn.ReLU()#激活函数\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = x.view(x.size(0), -1)#(32, 1, 28, 28)--->(32, 784)\n",
    "        x = self.fc1(x)#(32, 784)--->(32, 64)\n",
    "        x = self.relu(x)#激活(32, 64)\n",
    "        x = self.fc2(x)#(32, 64)--->(32, num_classes)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1652dc2c-d133-436b-8a70-fb789e760b41",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = NeuralNetwork(10)#实例化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ab0ad41e-4d07-4090-979f-77a255552692",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Linear-1                   [-1, 64]          50,240\n",
      "              ReLU-2                   [-1, 64]               0\n",
      "            Linear-3                   [-1, 10]             650\n",
      "================================================================\n",
      "Total params: 50,890\n",
      "Trainable params: 50,890\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 0.00\n",
      "Forward/backward pass size (MB): 0.00\n",
      "Params size (MB): 0.19\n",
      "Estimated Total Size (MB): 0.20\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "summary(model, input_size=(1, 28, 28))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1cfda04-37cd-48bf-aefc-f11a210ff2b6",
   "metadata": {},
   "source": [
    "## 3.超参数设置"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "67600298-da2b-4c95-97b2-aa43c18d16d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "learning_rate = 0.001#学习率\n",
    "epochs = 10#轮次"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c6e39cb9-d83b-4640-8c75-b82214762d48",
   "metadata": {},
   "outputs": [],
   "source": [
    "step = len(train_loader)#步长\n",
    "optim = torch.optim.Adam(model.parameters(), lr=learning_rate)#模型优化器\n",
    "cost = nn.CrossEntropyLoss()#交叉熵损失函数"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9bf44ea-7f88-444f-9d0d-4165709d5210",
   "metadata": {},
   "source": [
    "## 4.训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7f121e94-a774-4c8d-81a6-98399d4555f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 2), (1, 3), (2, 4)]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(enumerate([2,3,4]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26b5225a-f3e7-4a85-a780-585f6d02dbf3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Epoch:[5/10] Step:[1634/1875] Loss:[0.1]\n",
      "Epoch:[5/10] Step:[1635/1875] Loss:[0.02]\n",
      "Epoch:[5/10] Step:[1636/1875] Loss:[0.3]\n",
      "Epoch:[5/10] Step:[1637/1875] Loss:[0.16]\n",
      "Epoch:[5/10] Step:[1638/1875] Loss:[0.02]\n"
     ]
    }
   ],
   "source": [
    "for e in range(epochs):\n",
    "    for i, (images, labels) in enumerate(train_loader):\n",
    "        images = images.to(device)#把图像放到cpu上\n",
    "        labels = labels.to(device)#把标签放到cpu上\n",
    "        logits = model(images)#前向传播\n",
    "        loss = cost(logits, labels)#计算损失值\n",
    "        optim.zero_grad()#梯度清零\n",
    "        loss.backward()#反向传播\n",
    "        optim.step()#梯度下降，更新参数\n",
    "        print(\"Epoch:[{}/{}] Step:[{}/{}] Loss:[{}]\".format(e + 1, epochs, i + 1, step, round(loss.item(), 2)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8477514d-bb0a-4e7e-a33f-33f76705786c",
   "metadata": {},
   "source": [
    "## 5.模型加载"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "04d0216e-1d9b-48b8-a167-3240a403282d",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = torch.load(\"model.pth\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "141bac6a-5626-4059-b012-7193d35f37b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NeuralNetwork(\n",
       "  (fc1): Linear(in_features=784, out_features=64, bias=True)\n",
       "  (fc2): Linear(in_features=64, out_features=10, bias=True)\n",
       "  (relu): ReLU()\n",
       ")"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0754c8ca-6466-4c0c-b2d0-8b06edf35bfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "313"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_loader)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8e28e85-0cde-462a-9550-c491c7110f85",
   "metadata": {},
   "source": [
    "## 6.模型测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cd8502f3-96b2-4009-a76e-f501c31b00c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000张测试图像的准确率为：97.50%\n"
     ]
    }
   ],
   "source": [
    "with torch.no_grad():#以下代码中tensor没有grad梯度\n",
    "    total = 0 #总共的测试图像数量\n",
    "    correct = 0 #预测正确的图像数量\n",
    "    for (images, labels) in test_loader:\n",
    "        total += images.size(0)#累加图像数量，加batch size\n",
    "        logits = model(images)#获取预测结果\n",
    "        _, predicted = torch.max(logits, 1)#获取每一行最大值对应的下标\n",
    "        correct += torch.sum(predicted == labels).item()#累加预测正确的标签\n",
    "    print(\"{}张测试图像的准确率为：{:.2f}%\".format(total, (correct / total) * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8cd90b93-c601-4a5c-acc4-3d6f215466f9",
   "metadata": {},
   "source": [
    "## 7.提取参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "50a58517-51e8-49fb-b7a5-13ddafe644c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "params = dict(model.state_dict())#获取参数字典"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b24fb0bd-8399-46f6-a186-f45c33f7b523",
   "metadata": {},
   "outputs": [],
   "source": [
    "for key, value in params.items():\n",
    "    if len(value.size()) == 1:#判断tensor的维度\n",
    "        number = value.size(0)#偏置的参数个数\n",
    "    else:\n",
    "        number = value.size(0) * value.size(1)#权重的参数个数\n",
    "    param_string = str(value.tolist())#tensor转成list后再转字符串\n",
    "    param_string = param_string.replace(\"[\", \"\")#去掉[\n",
    "    param_string = param_string.replace(\"]\", \"\")#去掉]\n",
    "    file_name = key.replace(\".\", \"_\") + \".h\"#c语言头文件名字\n",
    "    head_string = \"float \" + key.replace(\".\", \"_\") + \"[\" + str(number) + \"]\" + \" = {\" + param_string + \"};\\n\"\n",
    "    with open(file_name, 'w') as f:\n",
    "        f.write(head_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f51b2475-09fa-469d-9406-bb134c9a8e7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "float fc1_weight[number] = {2.1, 3.14, ...};"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "173b5b59-0556-4eb6-98eb-4883884cc3f8",
   "metadata": {},
   "source": [
    "## 8.提取图像参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "76eace3c-546a-431c-aa09-5766d859e5ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "images, labels = next(iter(test_loader))#迭代一批数据出来，32张"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "22bf8126-bef6-4eae-b607-c428791d61f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image 7 saved.\n",
      "image 2 saved.\n",
      "image 1 saved.\n",
      "image 0 saved.\n",
      "image 4 saved.\n",
      "image 1 saved.\n",
      "image 4 saved.\n",
      "image 9 saved.\n",
      "image 5 saved.\n",
      "image 9 saved.\n",
      "image 0 saved.\n",
      "image 6 saved.\n",
      "image 9 saved.\n",
      "image 0 saved.\n",
      "image 1 saved.\n",
      "image 5 saved.\n",
      "image 9 saved.\n",
      "image 7 saved.\n",
      "image 3 saved.\n",
      "image 4 saved.\n",
      "image 9 saved.\n",
      "image 6 saved.\n",
      "image 6 saved.\n",
      "image 5 saved.\n",
      "image 4 saved.\n",
      "image 0 saved.\n",
      "image 7 saved.\n",
      "image 4 saved.\n",
      "image 0 saved.\n",
      "image 1 saved.\n",
      "image 3 saved.\n",
      "image 1 saved.\n"
     ]
    }
   ],
   "source": [
    "for img, lab in zip(images, labels):\n",
    "    number = img.size(1) * img.size(2)\n",
    "    image_string = str(img.tolist())#tensor转成list后再转字符串\n",
    "    image_string = image_string.replace(\"[\", \"\")#去掉[\n",
    "    image_string = image_string.replace(\"]\", \"\")#去掉]\n",
    "    head_string = \"float input_\" + str(lab.item()) + \"[\" + str(number) + \"] = {\" + image_string + \"};\\n\"#构建图像字符串\n",
    "    file_name = \"input_\" + str(lab.item()) + \".h\"#头文件名字\n",
    "    with open(file_name, 'w') as f:\n",
    "        f.write(head_string)\n",
    "    print(\"image {} saved.\".format(lab.item()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "684580da-56c5-49b2-9b9d-fc3f7720c330",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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